Species clustering in competitive Lotka-Volterra models

We study the properties of general Lotka-Volterra models with competitive interactions. The intensity of the competition depends on the position of species in an abstract niche space through an interaction kernel. We show analytically and numerically that the properties of these models change dramatically when the Fourier transform of this kernel is not positive definite, due to a pattern-forming instability. We estimate properties of the species distributions, such as the steady number of species and their spacings, for different types of interactions, including stretched exponential and constant kernels.     

Clustering gene expression data analysis using an improved EM algorithm based on multivariate elliptical contoured mixture models

Clustering gene expression data is an important research topic in bioinformatics because knowing which genes act similarly can lead to the discovery of important biological information. Many clustering algorithms have been used in the field of gene clustering. The multivariate Gaussian mixture distribution function was frequently used as the component of the finite mixture model for clustering, however the clustering cannot be restricted to the normal distribution in the real dataset. In order to make the cluster algorithm strong adaptability, this paper proposes a new scheme for clustering gene expression data based on the multivariate elliptical contoured mixture models (MECMMs). To solve the problem of over-reliance on the initialization, we propose an improved expectation maximization (EM) algorithm by adding and deleting initial value for the classical EM algorithm, and the number of clusters can be treated as a known parameter and inferred with the QAIC criterion. The improved EM algorithm based on the MECMMs is tested and compared with some other clustering algorithms, the performance of our clustering algorithm has been extensively compared over several simulated and real gene expression datasets. Our results indicated that improved EM clustering algorithm is superior to the classical EM algorithm and the support vector machines (SVMs) algorithm, and can be widely used for gene clustering.     

Classification of genes based on gene expression analysis

Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated. The text was submitted by the authors in English.     

Computational models for pigments analysis

The main objective of this study is to develop computational models and algorithms for automated image-based characterization of the types of pigments used in watercolours. Pigments constitute the main element of watercolours and such studies can provide important information related to the non-destructive examination of works of art. Semi-transparent pigments are very difficult to discriminate with non-destructive methods due to the reflective properties of the substrate; computer vision techniques can complement such traditional diagnostic methods by computing models and interpreting the visual properties of the pigments used. PACS 07.05.Pj; 42.30.Tz; 42.30.Va; 42.40.My; 89.20.Cf     

基于聚类技术的三维舰船模型特征库研究

存贮目标所有的视点图像,建立完备的特征库,或者提取能够抵抗视点变化的不变特征,是三维目标识别的常用方法。这两种方案都存在不足:要么特征库规模庞大,识别过程计算量大,识别效率低;要么难以找到鲁棒的识别特征。结合两种方案研究了基于聚类技术建立三维舰船模型特征库的方法。利用仿射传播聚类方法无需事先指定聚类中心的优点,将其应用于两型舰船模型的视点空间聚类。通过提取视点图像的Hu矩特征,进行了仿真实验,给出了聚类结果的有效性分析。     

Statistical analysis of gene regulatory networks reconstructed from gene expression data of lung cancer

Recently, inferring gene regulatory network from large-scale gene expression data has been considered as an important effort to understand the life system in whole. In this paper, for the purpose of getting further information about lung cancer, a gene regulatory network of lung cancer is reconstructed from gene expression data. In this network, vertices represent genes and edges between any two vertices represent their co-regulatory relationships. It is found that this network has some characteristics which are shared by most cellular networks of health lives, such as power-law, small-world behaviors. On the other hand, it also presents some features which are obviously different from other networks, such as assortative mixing. In the last section of this paper, the significance of these findings in the context of biological processes of lung cancer is discussed.     

Iterative temporal clustering analysis for the detection of multiple response peaks in fMRI

The temporal clustering analysis (TCA) is a novel and effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown. Performing the TCA method once can only detect the largest peak of the activation time windows well, if multiple response peaks at the same location of the brain occur. However, this limitation can be removed by using a TCA method in an iterative way in order for the smaller peaks to be detected. Our in vivo fMRI experiments with event-related visual tasks have demonstrated this ability.     

Multifractal detrended fluctuation analysis for clustering structures of electricity price periods

A new model is proposed to investigate the structure of electricity price in different time periods. A popular method — the multifractal detrended fluctuation analysis (MF-DFA) method is employed to analyze the features achieved from three types of electricity price data after filtering some trends by Fourier detrended fluctuation function. Twelve multifractal parameters are calculated and selected as the characteristic indicators for comparison. Moreover, the minimum number of indicators is determined so that the discriminant accuracy reaches maximum based on Fisher’s linear discriminant algorithm (Fisher’s LDA) for each time period. These indicators form a multi-dimensional space, in which each point represents a price time series. This allows us to cluster the three price time periods, namely, the low price time periods, the average price time periods and the peak price time periods. Fisher’s LDA is employed to evaluate the discriminant accuracy on these three kinds of time periods. Our analysis is then applied to the data in California1999–2000 and PJM2001–2002 electricity markets to demonstrate the applicability of our methods.     

Approximate models for the analysis of laser velocimetry correlation functions

Approximate models for data reduction to obtain turbulence levels are described. These aim to provide an alternative to earlier techniques which gave unsatisfactory results. An approximate iterative procedure, which assumes Gaussian turbulence, is in use in the Winfrith laser velocimeter system.     

滴状冷凝过程壁面反射光谱的分子团聚模型分析

根据反射光谱可用于分析固体表面介质凝聚状态的原理,理论分析了不锈钢表面上不同厚度薄液膜对表面反射率的影响,确定了在冷凝过程中该表面上冷凝液形成和更新过程导致相应反射率变化的范围. 通过分析滴状冷凝实验过程反射光谱的文献数据,研究了滴状冷凝过程壁面上蒸气分子凝聚特征,发现在实际的滴状冷凝传热过程中,液滴脱落后形成的裸露表面上存在反射特征介于液膜与体相蒸气分子之间的介质. 结合蒸气冷凝过程的分子团聚模型,得到了在滴状冷凝过程中近壁面附近的蒸气分子形成分子团聚分布的合理性. 此外,分析发现表面微观结构将改变团聚体分布密度,从而影响冷凝核化过程的现象. 这为冷凝传热强化方法的研究提出了新的思路. 关键词： 分子团聚 反射光谱 滴状冷凝     

Non-linear logit models for high-frequency data analysis

We analyze tick-by-tick data, the most high frequency data available, of yen–dollar exchange rates with focus on the direction of up or down price movement. We propose a non-linear logit model to describe a non-trivial probability structure, apparently invisible from the price change itself, shown in binarized data extracting up or down price movement. The model selected by AIC agrees well with empirical results. Additionally, the similar bias is obtained from binarized tick-by-tick data on NYSE, for example GE. Our model could be useful for a wide range of binary time series extracting their non-trivial probability structures.     

Herding and clustering: Ewens vs. Simon–Yule models

Clustering has often described by Ewens Sampling Formula (ESF). Focusing the attention on the evergreen problem of the size of firms, we discuss the compatibility of empirical data and ESF. In order to obtain a power law for all sizes in the present paper we shall explore the route inspired by Yule, Zipf and Simon. It differs from the Ewens model both for destruction and creation. In particular the probability of herding is independent on the size of the herd. Computer simulations seem to confirm that actually the mean number of clusters of size i (the equilibrium distribution) follows the corresponding Yule distribution. Finally we introduce a finite Markov chain, that resembles the marginal dynamics of a cluster, which drives the cluster to a censored Yule distribution.